A production inference checklist spans vLLM, SGLang, quantization, and load testing
ZeYanjie · x · 2026-07-29
A reposted checklist for building production-grade LLM inference stacks focuses on:
- learning vLLM and SGLang for high-throughput serving
- setting up paged attention, KV cache eviction, and speculative decoding
- understanding quantization tradeoffs such as INT4, FP8, AWQ, and GPTQ
- building model routers based on cost / latency / quality
- testing edge deployment options like ONNX, TensorRT, WebLLM, Ollama, LM Studio, and LiteLLM
- adding continuous batching, queue management, observability, load testing, and Kubernetes autoscaling
It frames inference as an end-to-end systems problem, not just a model problem.
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